LocTree2 - Protein sub-cellular localization prediction for all domains of life
Subcellular localization is one and easily definable aspect of protein function. Computational prediction of localization continues to provide an invaluable help especially in whole genome analyses and annotations. Several methods have been developed to predict localization, yet many challenges remain to be tackled.
We at RostLab developed a novel method, LocTree2 that predicts localization for all proteins in all domains of life. Similar to our previous method, LocTree, we incorporate a system of hierarchically organized Support Vector Machines to mimic the protein trafficking mechanism in cells. Please note that other than the hierarchy and the name LocTree and LocTree2 have nothing in common.
Amongst the novel aspects of LocTree2 are:
- the stunning number of 18 predicted classes for Eukaryota
- 6 classes for Bacteria and 3 classes for Archaea
- incorporation of no other information than evolutionary profiles
- very accurate in distinction: membrane/water-soluble globular proteins
- high robustness against sequencing errors
- top performance even for protein fragments
LocTree2 combines three different systems of classification trees to predict 3 localization classes in Archaea, 6 classes in Bacteria and 18 classes in Eukaryota (Figure 1).
Each hierarchy mimics the biological sorting mechanism in that domain (in eukaryotes membrane and non-membrane proteins are treated separately). The branches represent paths of the protein sorting, the leaves the final prediction of one localization class, and the internal nodes are the decision points along the path. These decisions are implemented as binary Support Vector Machines (SVMs)
Classification trees of SVMs
- The program can be accessed online via the PredictProtein service
- Standalone version can be downloaded as a zip file here
Data sets used for development and evaluation of LocTree2 can be accessed here.
For questions, please contact firstname.lastname@example.org